DOCSLIB.ORG

Biomed Research International Special Issue on Brain Computer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Biomed Research International Special Issue on Brain Computer BioMed Research International Special Issue on Brain Computer Interface Systems for Neurorobotics: Methods and Applications CALL FOR PAPERS Brain computer interface (BCI) systems establish a direct communication between Lead Guest Editor the brain and an external device. ese systems can be used for entertainment, to Victor H. C. De Albuquerque, improve the quality of life of patients and to control Virtual Reality applications, Universidade de Fortaleza, Fortaleza, industrial machines, and robots. In the neuroscience eld such as in neuroreha- Brazil bilitation, BCIs are integrated into controlled virtual environments used for the [email protected] treatment of disability, for example, cerebral palsy, Down syndrome, and depression. Its aim is to promote a recovery of brain function lost due to a lesion through Guest Editors noninvasive brain stimulation (brain modulation) in a more accurate and faster Robertas Damaševičius, Kaunas manner than the traditional techniques. Neurorobotics combines BCIs with robotics University of Technology, Kaunas, aiming to develop articial limbs, which can act as real members of human body Lithuania being controlled from a brain-machine interface. With the advancement of a better [email protected] understanding of how our brain works, new realistic computational algorithms are being considered, making it possible to simulate and model specic brain functions Nuno M. Garcia, University of Beira for the development of new Computational Intelligence algorithms and, nally, BCI Interior, Covilhã, Portugal for mobile devices/apps. [email protected] As an augmentative communication channel, BCI has already attracted considerable Plácido R. Pinheiro, Universidade de research interest thanks to recent advances in neurosciences, wearable biosensors, Fortaleza, Fortaleza, Brazil and data mining. However, to overcome numerous challenges BCI technology [email protected] still requires research in high-performance and robust signal processing and machine learning algorithms to produce a reliable and stable control signal from Pedro P. R. Filho, Ciência e Tecnologia nonstationary brain signals to allow development of real-life BCI systems usable do Ceará, Fortaleza, Brazil across many individuals. Further improvements to BCI systems are necessary to [email protected] ensure that they can meet the needs of specic user groups such as disabled or impaired people as well as common users. Manuscript Due Friday, çÔ March òýÔÞ is special issue focuses on recent advances and future trends in methods and applications of BCI systems evaluated in dierent elds of knowledge, such as First Round of Reviews neuroengineering, rehabilitation, psychology, pattern recognition, computational Friday, òç June òýÔÞ intelligence, machine learning, industrial engineering, control and automation engi- neering, and robotics. We are seeking theoretical, methodological, and particularly Publication Date empirical papers dealing with dierent topics. Friday, Ô August òýÔÞ Potential topics include but are not limited to the following: Acquisition of brain signals Brain signal processing algorithms Virtual, augmented, and mixed realities Automatic real time detection and diagnosis of disease Neurorobotics Mobile robot navigation Smart home Serious games and gamication Hardware, soware, and rmware Communication protocols Applications of BCI for education BCI for entertainment Privacy and security in BCI systems BCI systems for independent and assisted living Usability evaluation of BCI systems Authors can submit their manuscripts through the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/bmri/neuroscience/nsnbcis/..
Load more

Recommended publications
  • Acceptability Study of A3-K3 Robotic Architecture for a Neurorobotics Painting
    ORIGINAL RESEARCH published: 10 January 2019 doi: 10.3389/fnbot.2018.00081 Acceptability Study of A3-K3 Robotic Architecture for a Neurorobotics Painting Salvatore Tramonte 1*, Rosario Sorbello 1*, Christopher Guger 2 and Antonio Chella 1,3 1 RoboticsLab, Department of Industrial and Digital Innovation (DIID), Universitá di Palermo, Palermo, Italy, 2 G.tec Medical Engineering GmbH, Schiedlberg, Austria, 3 ICAR-CNR, Palermo, Italy In this paper, authors present a novel architecture for controlling an industrial robot via Brain Computer Interface. The robot used is a Series 2000 KR 210-2. The robotic arm was fitted with DI drawing devices that clamp, hold and manipulate various artistic media like brushes, pencils, pens. User selected a high-level task, for instance a shape or movement, using a human machine interface and the translation in robot movement was entirely demanded to the Robot Control Architecture defining a plan to accomplish user’s task. The architecture was composed by a Human Machine Interface based on P300 Brain Computer Interface and a robotic architecture composed by a deliberative layer and a reactive layer to translate user’s high-level command in a stream of movement for robots joints. To create a real-case scenario, the architecture was Edited by: Ganesh R. Naik, presented at Ars Electronica Festival, where the A3-K3 architecture has been used for Western Sydney University, Australia painting. Visitors completed a survey to address 4 self-assessed different dimensions Reviewed by: related to human-robot interaction: the technology knowledge, the personal attitude, the Calogero Maria Oddo, innovativeness and the satisfaction. The obtained results have led to further exploring the Scuola Sant’Anna di Studi Avanzati, Italy border of human-robot interaction, highlighting the possibilities of human expression in Valery E.
    [Show full text]
  • Ethical and Social Aspects of Neurorobotics
    Science and Engineering Ethics https://doi.org/10.1007/s11948-020-00248-8 ORIGINAL RESEARCH/SCHOLARSHIP Ethical and Social Aspects of Neurorobotics Christine Aicardi2 · Simisola Akintoye3 · B. Tyr Fothergill1 · Manuel Guerrero4,5 · Gudrun Klinker6 · William Knight1 · Lars Klüver7 · Yannick Morel8 · Fabrice O. Morin6 · Bernd Carsten Stahl1 · Inga Ulnicane1 © The Author(s) 2020 Abstract The interdisciplinary feld of neurorobotics looks to neuroscience to overcome the limitations of modern robotics technology, to robotics to advance our understand- ing of the neural system’s inner workings, and to information technology to develop tools that support those complementary endeavours. The development of these tech- nologies is still at an early stage, which makes them an ideal candidate for proac- tive and anticipatory ethical refection. This article explains the current state of neu- rorobotics development within the Human Brain Project, originating from a close collaboration between the scientifc and technical experts who drive neurorobotics innovation, and the humanities and social sciences scholars who provide contextu- alising and refective capabilities. This article discusses some of the ethical issues which can reasonably be expected. On this basis, the article explores possible gaps identifed within this collaborative, ethical refection that calls for attention to ensure that the development of neurorobotics is ethically sound and socially acceptable and desirable. Keywords Neurorobotics · Ethics · Responsible Research and Innovation
    [Show full text]
  • New Technologies for Human Robot Interaction and Neuroprosthetics
    University of Plymouth PEARL https://pearl.plymouth.ac.uk Faculty of Science and Engineering School of Engineering, Computing and Mathematics 2017-07-01 Human-Robot Interaction and Neuroprosthetics: A review of new technologies Cangelosi, A http://hdl.handle.net/10026.1/9872 10.1109/MCE.2016.2614423 IEEE Consumer Electronics Magazine All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. CEMAG-OA-0004-Mar-2016.R3 1 New Technologies for Human Robot Interaction and Neuroprosthetics Angelo Cangelosi, Sara Invitto Abstract—New technologies in the field of neuroprosthetics and These developments in neuroprosthetics are closely linked to robotics are leading to the development of innovative commercial the recent significant investment and progress in research on products based on user-centered, functional processes of cognitive neural networks and deep learning approaches to robotics and neuroscience and perceptron studies. The aim of this review is to autonomous systems [2][3]. Specifically, one key area of analyze this innovative path through the description of some of the development has been that of cognitive robots for human-robot latest neuroprosthetics and human-robot interaction applications, in particular the Brain Computer Interface linked to haptic interaction and assistive robotics. This concerns the design of systems, interactive robotics and autonomous systems. These robot companions for the elderly, social robots for children with issues will be addressed by analyzing developmental robotics and disabilities such as Autism Spectrum Disorders, and robot examples of neurorobotics research.
    [Show full text]
  • Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: a Systematic Literature Review
    sensors Systematic Review Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review Nuraini Jamil 1 , Abdelkader Nasreddine Belkacem 2,* , Sofia Ouhbi 1 and Abderrahmane Lakas 2 1 Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] (N.J.); sofi[email protected] (S.O.) 2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] * Correspondence: [email protected] Abstract: Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive Citation: Jamil, N.; Belkacem, A.N.; BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used Ouhbi, S.; Lakas, A. Noninvasive for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from Electroencephalography Equipment the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited for Assistive, Adaptive, and to studies focusing on applications and devices of the BCI technology.
    [Show full text]
  • Subproject 10: Neurorobotics
    Co-funded by the European Union Subproject 10: Neurorobotics Deliverable D10.7.2 Revised Report, 6 Oct 2017 D10.7.2 (D60.2 D25, SGA1 M12) ACCEPTED 20171108.docx PU = Public 08-Nov-2017 Page 1 of 124 Co-funded by the European Union Project Number: 720270 Project Title: Human Brain Project SGA1 Document Title: D10.7.2 SP10 Neurorobotics Platform - Results for SGA1 Period 1 Document Filename: D10.7.2 (D60.2 D25, SGA1 M12) ACCEPTED 20171108.docx Deliverable Number: SGA1 D10.7.2 Deliverable Type: Report Work Package(s): All WPs in SP10 Dissemination Level: PU = Public Planned Delivery SGA1 M12 / 31 Mar 2017 Date: Submitted: 31 May 2017 (M14), Resubmitted 6 Oct 2017 (M19), Accepted 08 Nov Actual Delivery Date: 2017 Authors: Alois KNOLL, TUM (P56), Marc-Oliver Gewaltig, EPFL (P1) Compiling Editors: Florian RÖHRBEIN, TUM (P56) Contributors: All Task leaders and their representatives SciTechCoord Re- EPFL (P1): Jeff MULLER, Martin TELEFONT, Marie-Elisabeth COLIN view: UHEI (P47): Martina SCHMALHOLZ, Sabine SCHNEIDER Editorial Review: EPFL (P1): Guy WILLIS, Colin McKINNON Abstract: This document describes the progress made in the first half of the first SGA. Keywords: Neurorobotics, virtual robotics, in silico experiments D10.7.2 (D60.2 D25, SGA1 M12) ACCEPTED 20171108.docx PU = Public 08-Nov-2017 Page 2 of 124 Co-funded by the European Union Table of Contents 1. Preamble ........................................................................................................ 8 2. SP Leader’s Overview........................................................................................
    [Show full text]
  • Editorial Brain Computer Interface Systems for Neurorobotics: Methods and Applications
    Hindawi BioMed Research International Volume 2017, Article ID 2505493, 2 pages https://doi.org/10.1155/2017/2505493 Editorial Brain Computer Interface Systems for Neurorobotics: Methods and Applications Victor Hugo C. de Albuquerque,1 Robertas DamaševiIius,2 Nuno M. Garcia,3 Plácido Rogério Pinheiro,1 and Pedro P. Rebouças Filho4 1 Programa de Pos´ Graduac¸ao˜ em Informatica´ Aplicada, Laboratorio´ de Bioinformatica,´ Universidade de Fortaleza, Fortaleza, CE, Brazil 2Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania 3Department of Informatics, Instituto de Telecomunicac¸oes˜ and ALLab Assisted Living Computing and Telecommunications Laboratory,UniversidadedaBeiraInterior,Covilha,˜ Portugal 4Laboratorio´ de Processamento de Imagens e Simulac¸ao˜ Computacional, Instituto Federal de Educac¸ao,˜ Cienciaˆ e Tecnologia do Ceara,´ Maracanau,´ CE, Brazil Correspondence should be addressed to Victor Hugo C. de Albuquerque; [email protected] Received 7 August 2017; Accepted 7 August 2017; Published 30 October 2017 Copyright © 2017 Victor Hugo C. de Albuquerque et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Brain computer interface (BCI) systems establish a direct data mining. However, to overcome numerous challenges the communication between the brain and an external device. BCI technology still requires research in high-performance These systems can be used for entertainment, to improve the and robust signal processing and machine learning algo- quality of life of patients and to control virtual and augmented rithmstoproduceareliableandstablecontrolsignalfrom reality applications, industrial machines, and robots. In the nonstationary brain signals to allow development of real- neuroscience field such as in neurorehabilitation, BCIs are life BCI systems usable across many individuals.
    [Show full text]
  • Human-Inspired Eigenmovement Concept Provides Coupling-Free Sensorimotor Control in Humanoid Robot
    ORIGINAL RESEARCH published: 25 April 2017 doi: 10.3389/fnbot.2017.00022 Human-Inspired Eigenmovement Concept Provides Coupling-Free Sensorimotor Control in Humanoid Robot Alexei V. Alexandrov 1, Vittorio Lippi 2, Thomas Mergner 2*, Alexander A. Frolov 1, 3, Georg Hettich 2 and Dusan Husek 4 1 Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia, 2 Department of Neurology, University Clinics of Freiburg, Freiburg, Germany, 3 Russian National Research Medical University, Moscow, Russia, 4 Institute of Computer Science, Academy of Science of the Czech Republic, Prague, Czechia Control of a multi-body system in both robots and humans may face the problem of destabilizing dynamic coupling effects arising between linked body segments. The state of the art solutions in robotics are full state feedback controllers. For human hip-ankle coordination, a more parsimonious and theoretically stable alternative to the robotics solution has been suggested in terms of the Eigenmovement (EM) control. Eigenmovements are kinematic synergies designed to describe the multi DoF system, and its control, with a set of independent, and hence coupling-free, scalar equations. This paper investigates whether the EM alternative shows “real-world robustness” against noisy and inaccurate sensors, mechanical non-linearities such as dead zones, and human-like feedback time delays when controlling hip-ankle movements of a balancing humanoid robot. The EM concept and the EM controller are introduced, the robot’s Edited by: dynamics are identified using a biomechanical approach, and robot tests are performed in Massimo Sartori, University of Göttingen, Germany a human posture control laboratory. The tests show that the EM controller provides stable Reviewed by: control of the robot with proactive (“voluntary”) movements and reactive balancing of Yury Ivanenko, stance during support surface tilts and translations.
    [Show full text]
  • A Hybrid and Scalable Brain-Inspired Robotic Platform
    www.nature.com/scientificreports OPEN A hybrid and scalable brain‑inspired robotic platform Zhe Zou1, Rong Zhao1, Yujie Wu1, Zheyu Yang1, Lei Tian1, Shuang Wu1, Guanrui Wang1, Yongchao Yu2, Qi Zhao1, Mingwang Chen1, Jing Pei1, Feng Chen2, Youhui Zhang3, Sen Song4, Mingguo Zhao1,2* & Luping Shi1* Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efciently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots. Here, we report a brain‑inspired robotic platform implemented by an unmanned bicycle that exhibits scalability of network scale, quantity and diversity to handle the changing needs of diferent scenarios. The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling fexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross‑ paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non‑spike form. The platform achieved various real‑time tasks concurrently in diferent real‑world scenarios, providing a new pathway to enhance robots’ intelligence. Humans have long aspired to develop an improved ability to handle multiple complex tasks in dynamic environ- ments. Robots represent a physical manifestation of intelligence, particularly when placed in dynamic complex environments to make decisions and predictions. Although the operating principles of the human brain remain largely unknown, neuroscientifc discoveries provide clues for designing intelligent robotic systems. Brain- inspired research has thus attracted widespread interest as a promising pathway for developing highly intelligent robotic platforms.
    [Show full text]
  • Embodied Models and Neurorobotics
    This is a repository copy of Embodied Models and Neurorobotics. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/130868/ Version: Accepted Version Book Section: Prescott, T.J. orcid.org/0000-0003-4927-5390, Ayers, J., Grasso, F.W. et al. (1 more author) (2016) Embodied Models and Neurorobotics. In: Arbib, M.A. and Bonaiuto, J., (eds.) From Neuron to Cognition via Computational Neuroscience. MIT Press , pp. 483-512. ISBN 9780262335270 Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Chapter 17. Embodied Models and Neurorobotics Tony J. Prescott, Department of Psychology, University of Sheffield, UK Joseph Ayers, Dept. of Biology & Marine Science Centre, Northeastern University, USA. Frank W. Grasso, Dept. of Psychology, Brooklyn College, USA, Paul F. M. J. Verschure, SPECS Lab, Universitat Pompeu Fabra & Catalan Institute of Advanced Research (ICREA), Spain. Corresponding author: Tony J Prescott, Dept. of Psychology, University of Sheffield, Western Bank, Sheffield, S10 2TN, UK.
    [Show full text]
  • EEG-Based Brain-Computer Interfaces (Bcis): a Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications
    1 EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications Xiaotong Gu, Zehong Cao*, Member, IEEE, Alireza Jolfaei, Member, IEEE, Peng Xu, Member, IEEE, Dongrui Wu, Senior Member, IEEE, Tzyy-Ping Jung, Fellow, IEEE, and Chin-Teng Lin, Fellow, IEEE Abstract—Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with the environment directly. Advances in neuroscience and computer science in the past decades have led to exciting developments in BCI, thereby making BCI a top interdisciplinary research area in computational neuroscience and intelligence. Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications. Many people benefit from EEG-based BCIs, which facilitate continuous monitoring of fluctuations in cognitive states under monotonous tasks in the workplace or at home. In this study, we survey the recent literature of EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensated for the gaps in the systematic summary of the past five years (2015-2019). In specific, we first review the current status of BCI and its significant obstacles. Then, we present advanced signal sensing and enhancement technologies to collect and clean EEG signals, respectively. Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track human cognitive states and operating performance in prevalent applications.
    [Show full text]
  • Postdoc Position in Neurorobotics at the SPECS-Lab Research Group
    Postdoc position in NeuroRobotics at the SPECS-lab Research Group Description of the position: The SPECS Research Group is offering a Postdoc position in NeuroRobotics. The successful candidate will lead SPECS’ NeuroRobotics lab and drive the research on Cognitive Architectures in the context of the Distributed Adaptive Control theory of mind and brain that SPECS-lab has been elaborating over many years [1, 2, 3, 4]. We are seeking a candidate to advance the DAC Architecture towards a whole brain implementation, which maps this cognitive architecture to the main structures of the mammalian brain. We will test these models on robots both targeting rodents level tasks such as foraging and navigation but also, we want to look at human level tasks such as social interaction and motor rehabilitation. The candidate will work directly with the director of SPECS-lab, ICREA Professor Dr. Paul Verschure. 1. Verschure, Paul FMJ. "Synthetic consciousness: the distributed adaptive control perspective." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1701 (2016): 20150448. 2. Verschure, PFMJ, Pennartz, CM, & Pezzulo, G. (2014). The why, what, where, when and how of goal-directed choice: neuronal and computational principles. Phil. Trans. R. Soc. B, 369(1655), 20130483. 3. Verschure, P. F. (2012). Distributed adaptive control: a theory of the mind, brain, body nexus. Biologically Inspired Cognitive Architectures, 1, 55-72. 4. Verschure, PFMJ T Voegtlin T, RJ Douglas (2003) - Environmentally mediated synergy between perception
    [Show full text]
  • Using Biophysical Computational Neural Models to Investigate
    Using Biophysical Computational Neural Models to Investigate Neuropsychiatric Disorders Samuel Neymotin, PhD Nathan Kline Institute for Psychiatric Research Orangeburg, NY Computational Psychiatry, 02/20/2020 From computational neuroscience → computational psychiatry . Psychiatry has fundamental problems in defining and diagnosing mental disorders/illness, partly due to obvious difficulties in measuring origins of symptoms (brain is largely inaccessible). In order to better understand, diagnose, and treat mental disorders, clinicians/scientists should leverage insights gained from the integrative methodologies used in computational and experimental neuroscience. However, computational neuroscience is a complex field, taking years of training. To allow computational neuroscience and psychiatry to make the largest impact, new computational/algorithmic tools are needed that integrate multiscale brain dynamics and behavior, and allow scientists to rapidly test their hypotheses on the neural origins of mental disorders. (Images from Wikipedia) Outline: modeling tools (solutions) & modeling case studies 1. Build open-source modeling tools (Human Neocortical Neurosolver: HNN) to help researchers understand circuit level origins of human brain dynamics (EEG/MEG) 2. Use auditory thalamocortical models to link functionally relevant brain rhythms in non-human primate to human electrophysiological data 3. Use hippocampal network models to study schizophrenia 4. Use multi-scale models of primary motor cortex to study neocortical hyperexcitability
    [Show full text]